20 citations · 23 across the 11 of their papers we have counts for
11 papers
Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies
Shalini Sarode, Muhammad Saif Ullah Khan, Tahira Shehzadi +2
We propose ClassroomKD, a novel multi-mentor knowledge distillation framework inspired by classroom environments to enhance knowledge transfer between the student and multiple ment…
Semi-Supervised Object Detection: A Survey on Progress from CNN to Transformer
Tahira Shehzadi, Ifza, Didier Stricker +1
The impressive advancements in semi-supervised learning have driven researchers to explore its potential in object detection tasks within the field of computer vision. Semi-Supervi…
Enhanced Bank Check Security: Introducing a Novel Dataset and Transformer-Based Approach for Detection and Verification
Muhammad Saif Ullah Khan, Tahira Shehzadi, Rabeya Noor +2
Automated signature verification on bank checks is critical for fraud prevention and ensuring transaction authenticity. This task is challenging due to the coexistence of signature…
UnSupDLA: Towards Unsupervised Document Layout Analysis
Talha Uddin Sheikh, Tahira Shehzadi, Khurram Azeem Hashmi +2
Document layout analysis is a key area in document research, involving techniques like text mining and visual analysis. Despite various methods developed to tackle layout analysis,…
End-to-End Semi-Supervised approach with Modulated Object Queries for Table Detection in Documents
Iqraa Ehsan, Tahira Shehzadi, Didier Stricker +1
Table detection, a pivotal task in document analysis, aims to precisely recognize and locate tables within document images. Although deep learning has shown remarkable progress in…
Towards End-to-End Semi-Supervised Table Detection with Semantic Aligned Matching Transformer
Tahira Shehzadi, Shalini Sarode, Didier Stricker +1
Table detection within document images is a crucial task in document processing, involving the identification and localization of tables. Recent strides in deep learning have subst…
A Hybrid Approach for Document Layout Analysis in Document images
Tahira Shehzadi, Didier Stricker, Muhammad Zeshan Afzal
Document layout analysis involves understanding the arrangement of elements within a document. This paper navigates the complexities of understanding various elements within docume…
Sparse Semi-DETR: Sparse Learnable Queries for Semi-Supervised Object Detection
Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker +1
In this paper, we address the limitations of the DETR-based semi-supervised object detection (SSOD) framework, particularly focusing on the challenges posed by the quality of objec…
Object Detection with Transformers: A Review
Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker +1
The astounding performance of transformers in natural language processing (NLP) has motivated researchers to explore their applications in computer vision tasks. DEtection TRansfor…
Bridging the Performance Gap between DETR and R-CNN for Graphical Object Detection in Document Images
Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker +2
This paper takes an important step in bridging the performance gap between DETR and R-CNN for graphical object detection. Existing graphical object detection approaches have enjoye…
Towards End-to-End Semi-Supervised Table Detection with Deformable Transformer
Tahira Shehzadi, Khurram Azeem Hashmi, Didier Stricker +2
Table detection is the task of classifying and localizing table objects within document images. With the recent development in deep learning methods, we observe remarkable success…